Question: a ) Consider a neural network for regression, t = y ( w , x ) + v , where v is Gaussian, i .

a) Consider a neural network for regression, t=y(w,x)+v, where v is Gaussian, i.e.,v(0,-1|), and w has a Gaussian priori, i.e.,(m0,-1I|). Assume that y(w,x) is the neural network output please derive the posterior distribution, (D,,m0,|), the posterior predictive distribution, (x,D,,m0,|), and the model evaluation, (,m0,|), where D={xn,tn},n=1,dots,N, is the training data set.
b) Consider a neural network for two-class classification, y=(a(w,x)) and a data set D={xn,tn}, where tnin{0,1},w has a Gaussian priori, i.e.,(0,-1I|), and a(w,x) is the neural network model. Please derive the posterior distribution, (D,|), posterior predictive distribution, (x,D,|), and the model evaluation, (|), respectively.
a ) Consider a neural network for regression, t =

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